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RNA-seq analysis

Most widely used web servers/software for single-cell RNA-seq analysis

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single-cell rna-seq

Gene expression in single cells is measured using single-cell RNA sequencing technology. It helps in finding new and available cell types of different tissues and organs. In this article, the most widely used web servers and software for single-cell RNA-seq analysis are discussed.

1. alona- It allows processing, analysis, and visualization. Additionally, it provides several other internal parameters such as normalization, filtering, clustering, batch correction, and many more. This web server is freely accessible at https://alona.panglaodb.se/ [1]

2. SCRAT- (Single Cell Regulome analysis toolbox) provides a graphical user interface (GUI) that uses single-cell transcriptome data to analyze cell heterogeneity, lineage relationships, and performs gene set enrichment analysis. It helps in the identification of cell subpopulations, cell identities from each subpopulation, and find new gene sets and transcription factors. The web server is accessible at https://zhiji.shinyapps.io/scrat and the software can be found at https://github.com/zji90/SCRAT [2].

3. ASAP- provides post-alignment analysis including normalization, filtering, parsing, and visualization of input data files. The automated Single-cell Analysis Pipeline (ASAP) is applicable to all RNA-seq data. The web server is available at asap.epfl.ch and scripts are available at github.com/DeplanckeLab/ASAP [3].

4. Granatum- also provides GUI to facilitate users who can analyze their data without using command-lines. It consists of multiple modules including gene-expression normalization, outlier removal, gene filtering, plate-merging, imputation, clustering, and so on. It is freely accessible at http://garmiregroup.org/granatum/app [4].

5. iS-CellR- (Interactive platform for single-cell RNA-sequencing) is another user-friendly web server for single-cell RNA-seq analysis. It provides some other features besides filtering and normalization that include dimensionality reductions, differential gene expressions to locate markers, inter-/intra-sample heterogeneity analysis, and access to R libraries [5]. The software is freely available at https://github.com/immcore/iS-CellR.


References

  1. Franzén, O., & Björkegren, J. L. (2020). alona: a web server for single-cell RNA-seq analysis. Bioinformatics.
  2. Ji, Z., Zhou, W., & Ji, H. (2017). Single-cell regulome data analysis by SCRAT. Bioinformatics33(18), 2930-2932.
  3. Gardeux, V., David, F. P., Shajkofci, A., Schwalie, P. C., & Deplancke, B. (2017). ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data. Bioinformatics33(19), 3123-3125.
  4. Zhu, X., Wolfgruber, T. K., Tasato, A., Arisdakessian, C., Garmire, D. G., & Garmire, L. X. (2017). Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists. Genome medicine9(1), 1-12.
  5. Patel, M. V. (2018). iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data. Bioinformatics34(24), 4305-4306.

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

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